基于频率比与集成学习的滑坡易发性评价——以金沙江上游巴塘—德格河段为例

EVALUATION OF LANDSLIDE SUSCEPTIBILITY BASED ON FREQUENCY RATIO AND ENSEMBLE LEARNING——TAKING THE BATANG-DEGE SECTION IN THE UPSTREAM OF JINSHA RIVER AS AN EXAMPLE

金沙江上游巴塘—德格河段地处青藏高原东部,该区地质、地形、地貌极其复杂,滑坡灾害最为发育,开展区域滑坡易发性评价对防灾减灾工作有着重要的意义。本文以金沙江上游巴塘—德格河段为研究区,在滑坡编录与野外实际调查的基础上,通过对滑坡分布规律和影响因素分析,选取高程、坡度、坡向、曲率、地形起伏度、地表切割度、地表粗糙度、地层岩性、断层、水系和道路等11个影响因子,构建了滑坡易发性评价指标体系。利用皮尔森系数去除高相关性影响因子,运用频率比方法定量分析各个因子与滑坡发育的关系。通过频率比模型选取非滑坡样本,采用集成学习算法模型进行滑坡易发性评价,根据易发性指数将研究区划分为极高易发区、高易发区、中易发区、低易发区及极低易发区5个等级。由滑坡易发性分区图和ROC曲线表明,高和极高易发区主要沿金沙江沿岸和沟谷分布,随机森林模型的成功率曲线下面积AUC=0.84,历史滑坡灾害位于高-极高易发区的灾害数占总滑坡数的84.8%,梯度提升树模型的成功率曲线下面积AUC=0.79,历史滑坡灾害位于高-极高易发区灾害数占总滑坡数的79.3%。由AUC值和历史灾害的分布可知,随机森林模型比梯度提升树模型在本研究区滑坡易发性评价中有着更好的评价精度和更高的预测能力。

The Batang-Dege section in the upstream of Jinsha River is located in the east of Qinghai-Tibet Plateau,where the geology,terrain and landform are extremely complex and the landslide hazards are developed well.The analysis of the regional landslide disasters susceptibility is significant to the landslide disaster prevention and mitigation.Taking the Batang-Dege section in the upstream of Jinsha River as the research area.It is based on the landslide record and field surveys.The 11 impact factors include the elevation,slope,aspect,curvature,relief amplitude,degree of surface cutting,surface roughness,stratum lithology,fault,road and water system.They are used to construct a landslide susceptibility evaluation system by analyzing the distribution law and influencing factors.The Pearson coefficient is calculated to remove the high-correlation impact factors.The frequency ratio method is used to analyze the relationship between each factor and the landslide development quantitatively.The frequency ratio model is applied to selecting non-landslide samples and the ensemble learning model is used to evaluate landslide susceptibility.According to the index of susceptibility,the search area is divided into five levels including extreme-highly susceptible area,highly susceptible area,moderately susceptible area,low susceptible area,and extreme-low susceptible area.As shown in the susceptibility divisional graph and ROC curve,the extreme-highly susceptible and highly susceptible area areas are mainly distributed along the banks of Jinsha River and ravines.The area under curve of success ratio of the Random Forest model is 0.84,the number of disasters located in extreme-highly and highly susceptible areas accounted for 84.8 percent of the total landslides.The area under curve of success ratio of the Gradient Boost Tree model is 0.79,the number of disasters located in extreme-highly and highly susceptible areas accounted for 79.3 percent of the total landslides.By the value of AUC and the distribution of historical disasters,it can be observed in the research area that the Random Forest model has better evaluation accuracy and higher prediction ability in landslide susceptibility evaluation than the Gradient Boost Tree model.